Convergent and Divergent Validity of the Child Pornography Offender Risk Tool (CPORT) in a Clinical Sample From California
Bibliographic record
Abstract
The Child Pornography Offender Risk Tool (CPORT) is a seven-item actuarial risk assessment tool that is used to estimate the potential for sexual recidivism among men convicted of child sexual exploitation material (CSEM; legally referred to as child pornography) offenses. In the current study, we examined the convergent and divergent validity of the CPORT in a clinical sample of 224 men on federal probation in the United States who were convicted of at least one type of CSEM offense. CPORT scores were significantly, moderately, and positively correlated with scores on another sexual offense risk assessment tool, the Risk Matrix 2000 (RM2000/S), showing broad evidence of convergent validity, and was nonsignificantly associated with scores on a general offense risk assessment tool, the Level of Service/Case Management Inventory (LS/CMI), showing evidence of divergent validity. There was also evidence of specific convergent validity; for example, the CPORT item reflecting prior criminal history was most strongly related to the Criminal History domain of the LS/CMI, and CPORT items reflecting sexual interest in children were significantly and strongly associated with self-reported sexual interest in children from the clinical evaluation. We also examined the impact of including clinical information in the scoring of the CPORT. Including this information reduced the amount of missing scores, but the impact on predictive accuracy is not yet known. Implications for clinical practices are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".